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UC 71 — One Crop Health

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Key Information
Title
One Crop Health for Next Generation Crop Protection
Acronym
One Crop Health
Coordinator
Københavns Universitet / University of Copenhagen (Denmark)University
Duration
24 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Sectors
Arable crop
Data Types
Machine sensor dataEarth observation dataFarm Management Information Systems (FMIS) data
SRIA Activities

3.1.2 Data integration and data quality

  • 3.1.2.1 Data acquisition & re-use framework
  • 3.1.2.2 Link databases & computing capacities
  • 3.1.2.3 Schemes for data interoperability
  • 3.1.2.4 Reference data sets & non-discriminatory data
  • 3.1.2.5 Standardized metadata scheme & ontologies
  • 3.1.2.6 Boost data re-usability through quality control
  • 3.1.2.7 Multi-layer geospatial data tool with API
  • 3.1.2.9 Granularity through smart systems & edge compute
  • 3.1.2.10 Error processing & quantifying methods
  • Models to increase data granularity
Themes
Crop production & monitoringPlant protection & pest/disease
Partners (1)
  • Københavns Universitet / University of CopenhagenP1
    Coordinator

    DenmarkUniversity

#OrganisationCountryType
P1Københavns Universitet / University of CopenhagenCoordinatorDenmarkUniversity
Test and experimental facilities (TEF research) data
Models & Macro data
Statistics Registers data
Public administration data
3.1.2.11
  • 3.1.2.13 Procedures to aggregate sensitive data

    • 3.1.3 Data marketplaces and cooperatives in agriculture

    • 3.1.3.1 Service Cloud & network of data-hubs
    • 3.1.3.3 Reward mechanisms for data sharing

    3.1.4 Applications of AI techniques

    • 3.1.4.1 Identify key reference/training data sets
    • 3.1.4.2 Capitalize historical satellite data
    • 3.1.4.3 Privacy law solutions for satellite imagery
    • 3.1.4.5 Data governance for farming data ownership
    • 3.1.4.6 Digital twins of farms & environments

    3.2.1 Enhancing functionality of and generating input for DSS including FMIS

    • 3.2.1.1 Data layers & algorithms for FMIS services
    • 3.2.1.2 Extrapolate farm-generated sensor data
    • 3.2.1.3 New satellite imagery & ground sensors for DSS
    • 3.2.1.4 Take stock of existing FMIS & their uptake

    3.2.2 Farm modelling systems

    • 3.2.2.1 Take stock of existing modelling approaches
    • 3.2.2.2 Novel forecasting & prediction methodologies
    • 3.2.2.3 Whole-farm & landscape environmental impact

    3.2.3 Assessment of farm performance

    • 3.2.3.1 Thematic areas for farm metrics
    • 3.2.3.2 Ambitious farm performance targets

    3.2.4 Data-based solutions for addressing environmental challenges

    • 3.2.4.1 Assess needs for environmental decision support
    • 3.2.4.2 Prescription maps for precision cropping

    3.2.5 Strategies and technologies for climate change adaptation

    • 3.2.5.1 Resilient livestock & cropping systems
    • 3.2.5.2 Lessons from other biogeographic regions

    3.3 Data-based solutions for policy-making

    • 3.3.1 Identify data needs for policy monitoring
    • 3.3.3 Common-approach indicators across MS

    4.1 Public-private synergies (R&I activities)

    • 4.1.1.1 Establish governance structures
    • 4.1.1.2 Map data needs in public & private domains

    4.2 Data governance, standards and security (R&I activities)

    • 4.2.4.1 Stock-take existing data ecosystems
    • 4.2.4.2 Privacy-preserving handling of personal data
    • 4.2.4.3 Harmonised access to public-sector data for research

    4.3 Uptake & innovation management (R&I activities)

    • 4.3.1.1 Evidence of the value of data technologies
    • 4.3.1.2 Communicate value to end-users
    • 4.3.1.3 User-friendly data platforms